A. Pre-trained, custom training, and reviewed.
B. Pre-trained. custom training, and fine-tunable.
C. Custom training, fine-tunable, and reviewed.
D. Pre-trained. fine-tunable, and reviewed.
A. Remove and restart.
B. Remove and kill.
C. Kill, remove, and restart.
D. Kill.
A. High coverage on the model means that fewer communications will be sent for manual review and that fewer automatable processes are missed.
B. The higher the coverage, the lower the model's recall, resulting in greater throughput of automatable processes.
C. High coverage ensures that the software consumes less computational resources, resulting in cost savings for the organization implementing the automation.
D. With high coverage, you can increase the amount of data provided downstream via Streams.
A. A solution for the processing of Excel files for extracting data tables.
B. A solution for combining different approaches to extract entities from Word documents such as contracts and agreements.
C. A solution for combining different approaches to extract information from workflows.
D. A solution that offers the ability to digitize, extract, validate, and train data from documents.
A. Underperforming labels.
B. Average label performance.
C. Balance.
D. Coverage.
A. Business Analysts.
B. Subject Matter Experts.
C. Automation RPA Developers.
D. Data Scientists.
A. Emails.
B. Invoices.
C. Letters.
D. Medical prescriptions.
A. The model that we create when training the platform to understand the data in those sources.
B. A permissioned storage area within the platform which contains communications and labels.
C. A collection of raw unlabeled communications data of a similar type, that can be associated with up to10 datasets.
D. A user-permissioned project containing a taxonomy with labels and entities.
A. Using a C/C++ IDE (Integrated Development Environment), then upload the code to Al Center IDE.
B. Using the Al Center model builder.
C. Using a Python IDE (Integrated Development Environment) or an AutoML platform.
D. Using the Al Center IDE (Integrated Development Environment).
A. A sub-field of artificial intelligence that enables systems to learn from data.Systems learn from previous experience and information to deduce and predict future information. To do this they use algorithms that learn to perform a specific task without being explicitly programmed.
B. The theory and development of computer systems that are able to perform tasks that normally require human intelligence and decision making.
C. An area of machine learning concerned with artificial neural networks.These are a series of algorithms that aim to recognize relationships in a set of data through a process that mimics biological neural networks.
D. A field of artificial intelligence that enables computers to gain high-level understanding from digital images or videos. If AI is the brain, then this is the eye that enables the computer to observe and understand. It works the same as the human eye.
